Dynamic Programming and Reinforcement Learning (Sommersemester 2023)

Lecturer: Dr. Rainer Schlosser (Enterprise Platform and Integration Concepts) , Alexander Kastius
Course Website: https://hpi.de/en/herbrich/teaching/dynamic-programming-and-reinforcement-learning.html

General Information

• Weekly Hours: 4
• Credits: 6
• Graded: yes
• Enrolment Deadline: 01.04.2023 - 07.05.2023
• Examination time §9 (4) BAMA-O: 18.07.2023
• Teaching Form: Seminar / Exercise
• Enrolment Type: Compulsory Elective Module
• Course Language: English

Programs, Module Groups & Modules

Description

The need for automated decision-making is steadily increasing. Hence, data-driven decision-making techniques are essential. We assume a system that follows certain dynamics and has to be tuned or controlled over time such that certain constraints are satisfied and a specified objective is optimized. Typically, the current state of the system as well as the interplay of rewards and potential future states associated to certain actions have to be taken into account. The dynamics and state transitions may have to be estimated from data using suitable ML-based techniques.

As, in general, exact solution approaches of such dynamic optimization problems do not scale often heuristics have to be used (e.g., in case the number of states is too large, cf. curse of dimensionality). Besides classical approaches such as dynamic programming (DP) state-of-the-art heuristic optimization techniques such as approximate dynamic programming (ADP) or reinforcement learning (RL) are suitable alternatives.

Goals of the Course

Understand...

• opportunities and challenges of decision-making
• static deterministic problems
• stochastic dynamic problems
• optimization models and solution techniques

Do ...

• work in small teams
• set up suitable models, apply optimization techniques 
• simulate controlled processes, compare performance results

Improve/Learn ...

• mathematical, analytical, and modelling skills
• optimization techniques
• dynamic programming methods
• reinforcement learning methods

Requirements

• interest in quantitative methods and stochastics
• programming skills/experience
• the number of participants is not restricted

Literature

• RL Introduction Book
• ADP Chapter
• Rainbow paper
• OpenAI Gym

Learning

The course is a combination of a lecture and a practical part:

• teachers impart relevant knowledge and methods
• students work on a self-containing topic in a team of ca. 3 people
• students present and document their work

Examination

Portfolio assessment for ITSE, DE, and DH-students consisting of:

• (i) final presentation of project results (July 18)
• (ii) project documentation at the end of the module (Sep 15)

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